J. R. Trapero, N. Kourentzes and R. Fildes, 2014, Journal of the Operantional Research Society, 66: 299-307. http://dx.doi.org/10.1057/jors.2013.174
Sales forecasting is of paramount importance to reduce inventory investment, enhance customer satisfaction and improve distribution operations. Shorter product life cycles, more competitive markets and more aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. One of the main reasons to use expert judgment is to forecast sales under promotional activity. An alternative approach to promotional forecasting is to replace expert adjustments by multivariate statistical models that use past promotions information, resulting in regression models whose exogenous inputs are promotion features (price discounts, type of display, type of advertising, etc.). Nonetheless, these regression type models may have a large dimension as well as multicollinearity issues. This work proposes a multivariate method that reduces the dimensionality of the problem by using Principal Component Analysis and models the error term as a transfer function identified by minimizing the Schwartz Information Criteria. To provide promotional forecasts for items with limited history we pool information across products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data from a manufacturing company; outperforming both substantially. We find that the proposed multivariate model, developed on the basis of past promotional information, outperforms expert promotional adjustments.